Abstract
Cardiovascular is nowadays a common and threatening disease for humans. Computer-aided diagnostic (CAD) can diagnose cardiovascular by finding anomalies in an electrocardiogram (ECG). However, this conventional diagnostic approach is inefficient and needs extensive analysis and medical knowledge to diagnose accurately. Deep learning can help in the timely detection of anomalies. ECG being a multi-tone signal, CNN-based temporal features are not sufficient for classification. A classification can be improved by integrating multi-spectral information with temporal features. Furthermore, the one-dimension (1D) ECG signal needs complex preprocessing, including signal cleaning and RR peak detection, whereas deep CNN is more efficient for 2D signals. This paper proposes an automated CAD system for ECG classification using a 2D deep-convolution network (CNN). The proposed CNN uses multi-spectral information by integrating wavelet-based spectral features with CNN's temporal features. The 1D ECG is reshaped to a 2D image, and a wavelet-encoded 2D CNN is proposed to classify these 2D images into four classes. The proposed model is evaluated using MIT-BIH datasets containing two-channel ambulatory ECG signals. A comparative study of both 1D and 2D approaches using deep CNN is presented in the paper initially. Later, the proposed network is validated by evaluating various quantitative parameters using the MIT-BIH dataset, and a comparative analysis is shown. The proposed CNN outperformed other models achieving the highest accuracy of 99.52% and 95.64% F1 score. The study reveals that the proposed model avoids the complex preprocessing of the ECG signals of cleaning, RR point detection, and waveform cropping. Further integration of wavelet-based multi-spectral features in CNN has improved the classification accuracy.